Operating an Autonomous Vehicle According to Road User Reaction Modeling with Occlusions

2. The method of claim 1, further comprising predicting, by the one or more computing devices, that the road agent will travel through at least one of the occluded one or more lane segments during the set time interval.

3. The method of claim 1, wherein determining the reaction time includes predicting when the nominal human actor associated with the road agent will react to the autonomous vehicle during or after the set time interval.

4. The method of claim 3, wherein predicting when the nominal human actor associated with the road agent will react to the autonomous vehicle includes determining when the field of view of the nominal human actor includes a minimum amount of the autonomous vehicle.

5. The method of claim 3, wherein predicting when the nominal human actor associated with the road agent will react to the autonomous vehicle includes determining that a location of the nominal human actor associated with the road agent in the occluded one or more lane segments is in a field of view of the detection system of the autonomous vehicle during or after the set time interval.

6. The method of claim 5, wherein determining that the location of the nominal human actor is in a field of view of the detection system of the autonomous vehicle during or after the set time interval includes determining whether a line segment between a lowest point of the autonomous vehicle and the location of the nominal human actor intersects an obstacle.

7. The method of claim 1, further comprising determining, by the one or more computing devices, the field of view of the nominal human actor based on a location of a human in the road agent detected by the detection system.

8. The method of claim 1, wherein determining the reaction time is further based on a location and an orientation of the road agent.

9. The method of claim 1, wherein determining the reaction time is further based on a location and orientation of the nominal human actor.

10. The method of claim 1, wherein determining the reaction time includes determining a predicted behavior of the road agent while the road agent is traveling in the occluded one or more lane segments.

13. The system of claim 12, wherein the one or more computing devices are configured to predict that the road agent will travel through at least one of the occluded one or more lane segments during the set time interval.

14. The system of claim 12, wherein the one or more computing devices are configured to determine the reaction time based on a predicted time when the nominal human actor associated with the road agent will react to the autonomous vehicle during or after the set time interval.

15. The system of claim 14, wherein the one or more computing devices are configured to determine the predicted time based on when the field of view of the nominal human actor includes a minimum amount of the autonomous vehicle.

16. The system of claim 14, wherein the one or more computing devices are configured to determine the predicted time based on when a location of the nominal human actor associated with the road agent is in the occluded one or more lane segments is in a field of view of the detection system of the autonomous vehicle during or after the set time interval.

17. The system of claim 16, wherein the one or more computing devices are configured to determine when the location of the nominal human actor is in a field of view of the detection system of the autonomous vehicle during or after the set time interval based on whether a line segment between a lowest point of the autonomous vehicle and the location of the nominal human actor intersects an obstacle.

18. The system of claim 12, wherein the one or more computing devices are configured to determine the field of view of the nominal human actor based on a location of a human in the road agent detected by the detection system.

19. The system of claim 12, further comprising the autonomous vehicle.